Towards Explainable Augmented Intelligence (AI) for Crack Characterization

Crack characterization is one of the central tasks of NDT&E (the Non-destructive Testing and Evaluation) of industrial components and structures. These days data necessary for carrying out this task are often collected using ultrasonic phased arrays. Many ultrasonic phased array inspections are...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Larissa Fradkin, Sevda Uskuplu Altinbasak, Michel Darmon
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Non-destructive Testing/Evaluation (NDT/NDE) ultrasonic imaging and inversion ultrasonic characterization explainable Augmented Intelligence Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/45b7dfb2b64e4fbfb866ad1a8f808505
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:45b7dfb2b64e4fbfb866ad1a8f808505
record_format	dspace
spelling	oai:doaj.org-article:45b7dfb2b64e4fbfb866ad1a8f8085052021-11-25T16:39:32ZTowards Explainable Augmented Intelligence (AI) for Crack Characterization10.3390/app1122108672076-3417https://doaj.org/article/45b7dfb2b64e4fbfb866ad1a8f8085052021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10867https://doaj.org/toc/2076-3417Crack characterization is one of the central tasks of NDT&E (the Non-destructive Testing and Evaluation) of industrial components and structures. These days data necessary for carrying out this task are often collected using ultrasonic phased arrays. Many ultrasonic phased array inspections are automated but interpretation of the data they produce is not. This paper offers an approach to designing an explainable AI (Augmented Intelligence) to meet this challenge. It describes a C code called AutoNDE, which comprises a signal-processing module based on a modified total focusing method that creates a sequence of two-dimensional images of an evaluated specimen; an image-processing module, which filters and enhances these images; and an explainable AI module—a decision tree, which selects images of possible cracks, groups those of them that appear to represent the same crack and produces for each group a possible inspection report for perusal by a human inspector. AutoNDE has been trained on 16 datasets collected in a laboratory by imaging steel specimens with large smooth planar notches, both embedded and surface-breaking. It has been tested on two other similar datasets. The paper presents results of this training and testing and describes in detail an approach to dealing with the main source of error in ultrasonic data—undulations in the specimens’ surfaces.Larissa FradkinSevda Uskuplu AltinbasakMichel DarmonMDPI AGarticleNon-destructive Testing/Evaluation (NDT/NDE)ultrasonic imaging and inversionultrasonic characterizationexplainable Augmented IntelligenceTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10867, p 10867 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Non-destructive Testing/Evaluation (NDT/NDE) ultrasonic imaging and inversion ultrasonic characterization explainable Augmented Intelligence Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	Non-destructive Testing/Evaluation (NDT/NDE) ultrasonic imaging and inversion ultrasonic characterization explainable Augmented Intelligence Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Larissa Fradkin Sevda Uskuplu Altinbasak Michel Darmon Towards Explainable Augmented Intelligence (AI) for Crack Characterization
description	Crack characterization is one of the central tasks of NDT&E (the Non-destructive Testing and Evaluation) of industrial components and structures. These days data necessary for carrying out this task are often collected using ultrasonic phased arrays. Many ultrasonic phased array inspections are automated but interpretation of the data they produce is not. This paper offers an approach to designing an explainable AI (Augmented Intelligence) to meet this challenge. It describes a C code called AutoNDE, which comprises a signal-processing module based on a modified total focusing method that creates a sequence of two-dimensional images of an evaluated specimen; an image-processing module, which filters and enhances these images; and an explainable AI module—a decision tree, which selects images of possible cracks, groups those of them that appear to represent the same crack and produces for each group a possible inspection report for perusal by a human inspector. AutoNDE has been trained on 16 datasets collected in a laboratory by imaging steel specimens with large smooth planar notches, both embedded and surface-breaking. It has been tested on two other similar datasets. The paper presents results of this training and testing and describes in detail an approach to dealing with the main source of error in ultrasonic data—undulations in the specimens’ surfaces.
format	article
author	Larissa Fradkin Sevda Uskuplu Altinbasak Michel Darmon
author_facet	Larissa Fradkin Sevda Uskuplu Altinbasak Michel Darmon
author_sort	Larissa Fradkin
title	Towards Explainable Augmented Intelligence (AI) for Crack Characterization
title_short	Towards Explainable Augmented Intelligence (AI) for Crack Characterization
title_full	Towards Explainable Augmented Intelligence (AI) for Crack Characterization
title_fullStr	Towards Explainable Augmented Intelligence (AI) for Crack Characterization
title_full_unstemmed	Towards Explainable Augmented Intelligence (AI) for Crack Characterization
title_sort	towards explainable augmented intelligence (ai) for crack characterization
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/45b7dfb2b64e4fbfb866ad1a8f808505
work_keys_str_mv	AT larissafradkin towardsexplainableaugmentedintelligenceaiforcrackcharacterization AT sevdauskuplualtinbasak towardsexplainableaugmentedintelligenceaiforcrackcharacterization AT micheldarmon towardsexplainableaugmentedintelligenceaiforcrackcharacterization
_version_	1718413072819814400

Towards Explainable Augmented Intelligence (AI) for Crack Characterization

Ejemplares similares